Description
This course will cover graduate-level materials on machine learning in both theory and practice by building upon the undergraduate-level course on ''Introduction to Machine Learning''. The topics include statistical learning theory, ensemble learning, probabilistic learning, dimension reduction, recommender systems, advanced clustering, semi-supervised learning, transfer learning, etc.